Re: st: gologit2 and mlogit coefficients do not agree

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 07:54 PM 2/12/2012, Rauscher, Garth wrote:
> Dear listservers,
>
> I am unable to reproduce the coefficients that I obtain from mlogit when I
> attempt to run the same model in gologit2. As a simplified example of the
> problem, my dependent variable (Y) has 3 categories (0,1,2) and I have a
> single binary independent variable X (0,1). Mlogit gave me the same result
> I obtained when I ran separate logistic regressions comparing Y=1 and Y=2
> separately with Y=0, but gologit2 did not. My results are below. At first
> I thought that gologit2 might be giving the inverse of mlogit but that is
> not the case.  I like the flexibility of gologit2 but am not sure how to
> interpret it's results.
>
> Thanks for listening, Garth
>
>
> . mlogit   y x , rrr baseoutcome(2)
>
> Multinomial logistic          Number of obs   =    730
>                               LR chi2(2)      =  25.52
>                               Prob > chi2     = 0.0000
> Log likelihood = -754.39125   Pseudo R2       = 0.0166
>
> -------------------------------------------------------
>             y |        RRR   Std. Err.      z    P>|z|
> -------------+-----------------------------------------
> 0           x |   .3853242   .1040091    -3.53   0.000
> 1           x |   .3950005   .0858599    -4.27   0.000
> 2             |  (base outcome)
> -------------------------------------------------------
>
>
> . gologit2 y x, npl or
>
> Generalized Ordered Logit    Number of obs   =    730
>                              LR chi2(2)      =  25.52
>                              Prob > chi2     = 0.0000
> Log likelihood = -754.39125  Pseudo R2       = 0.0166
>
> -------------------------------------------------------
>            y | Odds Ratio   Std. Err.      z    P>|z|
> -------------+-----------------------------------------
> 0           x |   1.822296   .4744057     2.31   0.021
> 1           x |   2.554348   .4826326     4.96   0.000
> -------------------------------------------------------

To elaborate on my earlier message -- mlogit is basically 0 vs 2 and 
1 vs 2. But gologit2 is like 0 versus 1 and 2 followed by 0 and 1 
versus 2. With unconstrained models like this the fits are often 
identical or nearly identical, but the parameterizations are different.


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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EMAIL:  Richard.A.Williams.5@ND.Edu
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